R Notebook

1. Introduction - Marika

In this section, explain why you chose this topic, and the questions you are interested in studying. Include a brief description of how you found the data, and clear instructions on where the reader can find the data.

2. Team - Adam

List team members and a description of how each contributed to the project. (If you’re working alone, briefly describe the stages of the project.)

1. Adam Coviensky -

2. Rohan Pitre -

3. Marika Lohmus -

3. Analysis of Data Quality - All

Zip Code, Neighborhood and Land Value Data - Adam

Sidewalk Cafe License Data - Marika

library(ggplot2)
library(dplyr)
library(tidyr)
library(extracat)
require(forcats)
library(ggmosaic)
library(ggmap)
library(gridExtra)
library(ggrepel)
library(lubridate)

sidewalks<-read.csv("Data/Sidewalk Cafes/Sidewalk_Licenses.csv",strip.white=TRUE, na.strings=c("","NA"))

Data Quality

One of the most glaring issues in data quality is the naming of the cities in Manhattan and Queens. Capitalization differences like between “NEW YORK” and “New York” grouped cafes in the same city to be categorized differently. Similarly, cities in Queens had some abbreviation differences like between “LONG ISLAND CITY” and “LONG IS CITY” or “JACKSON HEIGHTS” and “JACKSON HTS”. I manually replaced all abbreviated or non-capitalized cities with their longer, capitalized forms.

sidewalks$CITY[sidewalks$CITY=="LONG IS CITY"]<-"LONG ISLAND CITY"
sidewalks$CITY[sidewalks$CITY=="MIDDLE VLG"]<-"MIDDLE VILLAGE"
sidewalks$CITY[sidewalks$CITY=="JACKSON HTS"]<-"JACKSON HEIGHTS"
sidewalks$CITY[sidewalks$CITY=="New York"]<-"NEW YORK"

The second data quality issue I encountered was the longitude and latitude of two restaurants. Plotting all restaurants using qmplot, I got the following result:

qmplot(LONGITUDE,LATITUDE,data=sidewalks,maptype="toner-lite",color=I("purple"))

Using zoom = 9...
`panel.margin` is deprecated. Please use `panel.spacing` property instead

Note that there is a mysterious dot all the way west of Harrisburg, PA. This should not be the case since the data should only apply to the City and boroughs of New York, so I plotted the longitude and latitude to see any outliers.

lat<-ggplot(sidewalks, aes(x=LATITUDE))+geom_histogram(binwidth=.01)+ggtitle("Latitude Histogram")
long<-ggplot(sidewalks,aes(x=LONGITUDE))+geom_histogram(binwidth=.01)+ggtitle("Longitude Histogram")
grid.arrange(lat,long,nrow=2)

You can barely see some points beow the 40.25 latitude and -77 longitude, which seem to be quite off from the rest. Next, I zoomed in on those values:

lat<-ggplot(sidewalks, aes(x=LATITUDE))+geom_histogram(binwidth=.25)+ggtitle("Latitude Histogram")+xlim(39,40.25)
long<-ggplot(sidewalks,aes(x=LONGITUDE))+geom_histogram(binwidth=.25)+ggtitle("Longitude Histogram")+xlim(-80,-77)
grid.arrange(lat,long,nrow=2)

There are two datapoints that have an abnormally low Latitude and Longitude as compared to the rest of the data. Taking a look at these data points, I identified them as the following businesses:

sidewalks %>% filter(LONGITUDE < -77) %>% select(BUSINESS_NAME2,LONGITUDE,LATITUDE)

Looking these locations up on Google, it looks like there was an error in entering their longitude and latitude. The correct values are (40.72017,-73.9968) for Mulberry Street Bar and (40.79593,-73.9357) for Prime One 16. I have corrected these values in another CSV, which I will use from here on.

sidewalks<-read.csv("Data/Sidewalk Cafes/Sidewalk_Licenses2.csv",strip.white=TRUE, na.strings=c("","NA"))
sidewalks$CITY[sidewalks$CITY=="LONG IS CITY"]<-"LONG ISLAND CITY"
sidewalks$CITY[sidewalks$CITY=="MIDDLE VLG"]<-"MIDDLE VILLAGE"
sidewalks$CITY[sidewalks$CITY=="JACKSON HTS"]<-"JACKSON HEIGHTS"
sidewalks$CITY[sidewalks$CITY=="New York"]<-"NEW YORK"
qmplot(LONGITUDE,LATITUDE,data=sidewalks,maptype="toner-lite",color=I("purple"))

Using zoom = 12...
`panel.margin` is deprecated. Please use `panel.spacing` property instead

Now that the offending datapoints have been fixed, the qmplot accurately zooms in on the New York area. However, since those two data points exist, it is entirely possible that there are other mis-mapped langitude and longitude points. However, without manually going through each data point, it is not easy to deduce where.

Missing Data

To further investigate data quality, I took a look at the main columns that identified a business - its license number, license status, business name (broken into two), building number, street, city, state and zip. To display this data, I used a visna plot which highlights any columns with missing values and shows the frequency of the combinations of those columns.

visna(sidewalks[,1:9])

The visna shows that only two variables have missing values - license number and business name number 2. These are expected - only licenses that have been approved would have a license number, and some businesses do not need to use a second line for their business name.

Next, I looked at additional application information which includes the sidewalk cafe type, the square footage requested, number of tables, number of chairs, Department of Health and Mental Hygiene (DOHMH) identification number, the restaurant’s longitude and latitude, the community district and city council district it belongs to, and the URL for the website of the NYC Community District.

visna(sidewalks[,10:19])

Two variables have missing values - the square footage of the sidewalk cafe and the Department of Health and Mental Hygene identification number. These are items that the restaurant would have to provide during their application process, and may not have been available at submission. These missing values should not impede with our analysis. However, if we wanted to cross-reference the health grades from the DOHMH datasets, we would have issues with the missing ID numbers.

The next set of columns to analyze is the set of application-specific fields. These include the application ID, and the sidewalk cafe type, square footage, number of tables and chairs provided by the applicant. It has the app status, the date at which the app status was reached, expiration date, the expiration date of the temporary operating order(“TOO” - if available), the application submit date, and whether the application has been received.

visna(sidewalks[,20:29])

There seem to be a few missing fields in the provided sidewalk cafe squarefootage data. However, we will not be focusing on this data point in our analysis. Expiration dates are also missing from certain licenses. Filtering out those licenses and looking at thei APP_STATUS, we can see that those licenses that are in review may not have an assigned expiration status. Indeed, this makes sense with new applications that have not been given an expiration date.

missing_expiration<-sidewalks %>% filter(is.na(EXPIRATION_DATE)) %>% select(APP_STATUS)
ggplot(missing_expiration,aes(x=APP_STATUS))+geom_bar()+ggtitle("In Review licenses have a missing expiration date")

A similar explanation arises about the applications with a missing Temporary Operating Order (“TOO”) date - only those applications that have been granted a TOO in cases where the old license has expired but the renewal is in the process of being reviewed.

The remaining colums track the status of the license approval process through various stages and will not be used in this analysis. Therefore, I will not spend time on investigating any missing data.

Combining with Neighborhood data

From Adam’s analysis, we have a master_zip list of all of the zip codes mapped to the neighborhoods in each borough. I will create a separate dataframe called sidewalks_nbh which is joined with the master_zip on Zip Code. I will use the neighborhood column, ‘nbh’, to group the sidewalk cafes by borough and neighborhood. I then filtered the resulting dataframe to find any missing values from the join.

master_zip<-read.csv("Data/zips_master_no_missing_nbrh.csv", strip.white=TRUE)
sidewalks_nbh <- merge(sidewalks, master_zip, by="ZIP", all.x=TRUE)
sidewalks_nbh %>% filter(is.na(nbh)) %>% select(ZIP)

There were a lot of missing values originally from the master_zip analysis, which I have manually researched and filled in in the _nbrh.csv. Now there are no Cafes without a neighborhood designation.

Liquor License Data - Rohan

4. Executive Summary - All

Interactive Map of New York City

Provide a short nontechnical summary of the most revealing findings of your analysis with no more than 3 static graphs or one interactive graph (or link), written for a nontechnical audience. The length should be approximately 2 pages (if we were using pages…) Do not show code, and take extra care to clean up your graphs, ensuring that best practices for presentation are followed. . Note: the tips below are not intended to be a complete list of everything we’ve covered this semester on designing a successful graph. It’s meant to help you avoid some common problems. . Title, axis labels, tick mark labels, and legends should be comprehensible (easy to understand) and legible (easy to read / decipher). . Tick marks should not be labeled in scientific notation or with long strings of zeros, such as 3000000000. Instead, convert to smaller numbers and change the units: 3000000000 becomes “3” and the axis label “billions of views”. . Units should be intuitive (Extreme example: an axis labeled in month/day/year format is intuitive, one labeled in seconds since January 1, 1970 is not.) . The font size should be large enough to read clearly. The default in ggplot2 is generally too small. You can easily change it by passing the base font size to the theme, such as + theme_grey(16). (The default base font size is 11.) . The order of items on the axes and legends is logical. (Alphabetical is often not logical.) . Colors should be color vision deficiency friendly. . If a legend is taking up too much space on the right, move it to the bottom. . If categorical variable levels are long, set up the graph so the categorical variable is on the y-axis and the names are horizontal. A better option, if possible, is to shorten the names of the levels. . Not all EDA graphs lend themselves to presentation, either because the graph form is hard to understand without practice or it’s not well labeled. The labeling problem can be solved by adding text in an image editor. The downside is that it is not reproducible. If you want to go this route, Paintbrush is a free and simple bitmap image editor for the Mac: https://paintbrush.sourceforge.io/ There are many other options.

5. Main Analysis - All

Provide a detailed, well-organized description of your findings, including textual description, graphs, and code. Your focus should be on both the results and the process. Include, as reasonable and relevant, approaches that didn’t work, challenges, the data cleaning process, etc. . The guidelines for the Executive Summary above do NOT apply to exploratory data analysis. Your main concern is designing graphs that reveal patterns and trends. . As noted in Hmk #4, do not use circles, that is: bubbles, pie charts, or polar coordinates. . Use stacked bar charts sparingly. Try grouped bar charts and faceting as alternatives, and only choose stacked bar charts if they truly do a better job than the alternatives for observing patterns.

Zip Code, Neighborhood and Land Value Data - Adam

Sidewalk Cafe License Data - Marika

Any business that operates a portion of a restaurant on a public sidewalk must obtain a Sidewalk Cafe License from New Yor City. These licenses must be renewed every two years and fall into three categories: enclosed, unenclosed, or small unenclosed sidewalk cafes.

Analyzing by Borough

First, to help better organize the sidewalk cafe licenses by borough, I added a new column called BOROUGH that is set to MANHATTAN, BROOKLYN, BRONX, or QUEENS. I had to manually check that only the cities in Queens had been called out specifically in the CITY column, so it was easy to distinguish them from BRONX or BROOKLYN.

sidewalks <- sidewalks %>% mutate(BOROUGH = ifelse(CITY=="NEW YORK"|CITY=="New York","MANHATTAN",ifelse(CITY=="BROOKLYN","BROOKLYN",ifelse(CITY=="BRONX","BRONX","QUEENS"))))

To get a better understanding of the distribution of these licenses, I have provided a bar graph by borough.

ggplot(sidewalks, aes(x=fct_infreq(BOROUGH)))+geom_bar(aes(fill=BOROUGH))+ggtitle("Frequency of Sidewalk Cafe Licenses by Borough")+xlab("Borough")+ylab("Frequency")

Clearly Manhattan has the most license requests, followed by Brooklyn, then Queens and finally Bronx. Since at the moment we don’t have neighborhood information (everything in Manhattan is just classified as New York, Brooklyn has only Brooklyn, and Bronx has only the city of Bronx), we can only dive into the Queens data:

queens_cafes <- sidewalks %>% filter(BOROUGH=="QUEENS")
ggplot(queens_cafes, aes(x=fct_infreq(CITY)))+geom_bar(fill="purple")+ggtitle("Frequency of Sidewalk Cafe Licenses in Queens")+xlab("City / Neighborhood")+ylab("Frequency")+coord_flip()

In queens, a large percentage of license requests come from Astoria, followed by Long Island City and Forest Hills.

Next, in order to do date comparisons to ascertain which are the new applications vs. renewal applications, I had to convert certain date fields from strings (they were read in as string factors) into dates.

sidewalks$EXPIRATION_DATE<-as.Date(sidewalks$EXPIRATION_DATE, format="%m/%d/%Y")
sidewalks$APP_STATUS_DATE<-as.Date(sidewalks$APP_STATUS_DATE, format="%m/%d/%Y")
sidewalks$SUBMIT_DATE<-as.Date(sidewalks$SUBMIT_DATE, format="%m/%d/%Y")

Licenses by Status

The list of licenses includes active licenses, expired licenses, licenses for businesses that have closed (and are now inactive), licenses which are up for renewal as part of the two year process, or new requests for licenses. To better classify them, I created a new field called STATUS_CLASSIFICATION. Those licenses which are still active and not up for renewal are classified as “ACTIVE”. Those licenses that have been submitted for renewal (either because their expiration date is less than the latest application data, or that an active license is up for review) are classified as “RENEWAL”. Those licenses that are in the sheet but do not have a license number are classified as “NEW”, and the rest are marked as “OLD” to encompass inactive licenses that have not been acted upon.

sidewalks<-sidewalks %>% mutate(STATUS_CLASSIFICATION = ifelse(LIC_STATUS=="Active" & (APP_STATUS=="Application Approved" | APP_STATUS=="Application Review Completed"),"ACTIVE",ifelse(is.na(LICENSE_NBR),"NEW",ifelse((APP_STATUS_DATE>EXPIRATION_DATE | DPQA=="Issued Temp Op Letter") | (LIC_STATUS=="Active" & (APP_STATUS=="Pending Review" | APP_STATUS=="Submitted")),"RENEWAL","OLD"))))

Now that we have classified the status of the licenses, we are able to see how these classifications differ between the boroughs.

ggplot(sidewalks)+geom_mosaic(aes(x=product(STATUS_CLASSIFICATION,BOROUGH),fill=factor(STATUS_CLASSIFICATION)))+coord_flip()+labs(x="Borough",y="License Status", fill="License Designation")+ggtitle("Boroughs by License Status")

The mosaic plot shows how Bronx and Brooklyn may be getting more new license requests as a percentage of total licenses. Bronx is also getting the highest percentage of renewal requests out of its inactive and active licenses. We can also take a look at the license designations by borough:

ggplot(sidewalks)+geom_mosaic(aes(x=product(BOROUGH,STATUS_CLASSIFICATION),fill=factor(BOROUGH)))+coord_flip()+labs(x="License Status",y="Borough", fill="Borough")+ggtitle("License Status by Borough")

Looking at the data in this way, you can see how Brooklyn has the second-most new license requests, but how Manhattan still dominates in all license status categories.

Mapping Licenses

We can map the data to have a better view of where the datapoints lie. To get an overall picture, I selected a map centered on Long Island City in Queens so that we can get a good view of both Brooklyn and Bronx in addition to Manhattan.

map <- get_map( location = c(-73.9485424, 40.7454513),  source = "stamen", zoom = 11, maptype="toner") 

Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=40.745451,-73.948542&zoom=11&size=640x640&scale=2&maptype=terrain&sensor=false
Map from URL : http://tile.stamen.com/toner/11/602/768.png
cannot remove file 'fa373044657f7c62d16741b55e210dc9.rds', reason 'No such file or directory'Map from URL : http://tile.stamen.com/toner/11/603/768.png
cannot remove file 'b7bd091d8547e6622f48ce421780b6bc.rds', reason 'No such file or directory'Map from URL : http://tile.stamen.com/toner/11/604/768.png
cannot remove file 'e9000e2559ec9b9ab2bae536d0f686ee.rds', reason 'No such file or directory'Map from URL : http://tile.stamen.com/toner/11/602/769.png
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Plotting each of the restaurants colored by their borough. You can see how Manhattan dominates in the number of sidewalk cafes, and how the sidewalk cafes in Brooklyn and Queens are largely concentrated in the areas closer to Manhattan.

ggmap(map)+geom_point(aes(x=LONGITUDE,y=LATITUDE, color=BOROUGH),data=sidewalks, alpha=0.3)+ggtitle("Distribution of all licenses (inactive or active) in NYC")

Even with alpha, it is difficult to tell exactly where the largest concentrations of sidewalk cafes lie. Using a density plot, we can better see the concentration of sidewalk cafes around mid-to lower Manhattan, and the clusters in Astoria and Williamsburg.

g <- ggmap(map)+stat_density2d(aes(x=LONGITUDE,y=LATITUDE, fill=..level..),data=sidewalks, geom="polygon", alpha=0.2)
g+scale_fill_gradient(low="yellow",high="red")+ggtitle("Heatmap of Sidewalk Cafe Licenses in NYC")

The next question we can ask is whether there are clear patterns to where new license requests are coming in from, where they are being renewed, or where they have expired without renewal.

ggmap(map)+geom_point(aes(x=LONGITUDE,y=LATITUDE, color=BOROUGH),data=sidewalks, alpha=0.4)+facet_wrap(~STATUS_CLASSIFICATION)+ggtitle("Sidewalk Cafe Licenses by Status")

However, since we are zoomed out a lot and looking at the entire set of licenses, it is difficult to tell the difference between the distributions of license requests. For this analysis, I am only concerned about active licenses (whether they are being renewed or not), and new requests that have not yet been approved. To do this, I created another STATUS_CLASSIFICATION that groups active renewal requests into the Active category, and sets all other non-new requests as “inactive”.

sidewalks <- sidewalks %>% mutate(STATUS_CLASSIFICATION2=ifelse(STATUS_CLASSIFICATION=="ACTIVE" | (STATUS_CLASSIFICATION=="RENEWAL" & LIC_STATUS=="Active"), "ACTIVE", ifelse(STATUS_CLASSIFICATION=="NEW","NEW","OLD")))
new_active <- sidewalks %>% filter(STATUS_CLASSIFICATION2=="ACTIVE" | STATUS_CLASSIFICATION2=="NEW")
ggmap(map)+geom_point(aes(x=LONGITUDE,y=LATITUDE, color=BOROUGH),data=new_active, alpha=0.3)+facet_wrap(~STATUS_CLASSIFICATION2)+ggtitle("Active and New Licenses in New York City")

Since we are quite zoomed out, it is difficult to see what exactly is happening with the New requests. However, if we zoomed in, we would lose information about the Bronx or lower Brooklyn. In order to determine whether there is any useful information there, I have zoomed in on the Brooklyn region.

Geographic Analysis - Bronx

bronx_map <- get_map("Bronx, NY",  source = "google", zoom = 12, maptype="roadmap", color="bw") 

Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=Bronx,+NY&zoom=12&size=640x640&scale=2&maptype=roadmap&language=en-EN&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Bronx,%20NY&sensor=false

ggmap(bronx_map)+geom_point(aes(x=LONGITUDE,y=LATITUDE, color=BOROUGH),data=new_active)+facet_wrap(~STATUS_CLASSIFICATION2)+ggtitle("Active and New Licenses in the Bronx")

Since there are very few active or new licenses in the Bronx, I feel comfortable in zooming in on the rest of Manhattan / Brooklyn and Queens in order to be able to better see what is happening at the expense of Bronx.

Geographic Analysis - Manhattan

I want to take a closer look at what is happening in Manhattan. In order to do this at a more granular level, I will use my merged data with our master_zip document which lists the neighborhoods of each Borough by zip code. I also pulled out the year of the submission of the license application and saved it as SUBMIT_YEAR.

master_zip<-read.csv("Data/zips_master_no_missing_nbrh.csv", strip.white=TRUE)
sidewalks_nbh <- merge(sidewalks, master_zip, by="ZIP", all.x=TRUE)
sidewalks_nbh <- sidewalks_nbh %>% mutate(SUBMIT_YEAR=year(SUBMIT_DATE))
sidewalks_manhattan_active <- sidewalks_nbh %>% filter(BOROUGH=="MANHATTAN" & (STATUS_CLASSIFICATION2=="ACTIVE" | STATUS_CLASSIFICATION2=="NEW"))
sidewalks_brooklyn_active <- sidewalks_nbh %>% filter(BOROUGH=="BROOKLYN" & (STATUS_CLASSIFICATION2=="ACTIVE" | STATUS_CLASSIFICATION2=="NEW"))
sidewalks_bronx_active <- sidewalks_nbh %>% filter(BOROUGH=="BRONX" & (STATUS_CLASSIFICATION2=="ACTIVE" | STATUS_CLASSIFICATION2=="NEW"))
sidewalks_queens_active <- sidewalks_nbh %>% filter(BOROUGH=="QUEENS" & (STATUS_CLASSIFICATION2=="ACTIVE" | STATUS_CLASSIFICATION2=="NEW"))
manhattan_map <- get_map("Manhattan, NY", source="google", maptype="roadmap", zoom=12, color="bw")

Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=Manhattan,+NY&zoom=12&size=640x640&scale=2&maptype=roadmap&language=en-EN&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Manhattan,%20NY&sensor=false

ggmap(manhattan_map)+geom_point(aes(x=LONGITUDE, y=LATITUDE, color=nbh), data=sidewalks_manhattan_active)+ggtitle("All active or new sidewalk cafes in Manhattan")

manhattan_names <- sidewalks_manhattan_active %>% distinct(nbh, latitude, longitude) %>% group_by(nbh) %>% summarise(mean_lon=mean(longitude), mean_lat=mean(latitude))
brooklyn_names <- sidewalks_brooklyn_active %>% distinct(nbh, latitude, longitude) %>% group_by(nbh) %>% summarise(mean_lon=mean(longitude), mean_lat=mean(latitude))
queens_names <- sidewalks_queens_active %>% distinct(nbh, latitude, longitude) %>% group_by(nbh) %>% summarise(mean_lon=mean(longitude), mean_lat=mean(latitude))
bronx_names <- sidewalks_bronx_active %>% distinct(nbh, latitude, longitude) %>% group_by(nbh) %>% summarise(mean_lon=mean(longitude), mean_lat=mean(latitude))

This view shows all of the distribution of all currently active or new sidewalk cafes in Manhattan. To get a better idea of density, we can also look at a heatmap of this view.

g <- ggmap(manhattan_map)+stat_density2d(aes(x=LONGITUDE,y=LATITUDE, fill=..level..),data=sidewalks_manhattan_active, geom="polygon", alpha=0.3)
g+scale_fill_gradient(low="yellow",high="red")+ggtitle("Heatmap of Sidewalk Cafe Licenses in Manhattan")+geom_text_repel(data=manhattan_names, aes(mean_lon, mean_lat, label=nbh),fontface="bold", size=3)

This heatmap shows the highest concentration of sidewalk cafes in Greenwich Village and NoHo. To see the difference between active and new licenses, we can facet based on our previously calculated categorization.

g <- ggmap(manhattan_map)+stat_density2d(aes(x=LONGITUDE,y=LATITUDE, fill=..level..),data=sidewalks_manhattan_active, geom="polygon", alpha=0.3)+facet_wrap(~STATUS_CLASSIFICATION2)
g+scale_fill_gradient(low="yellow",high="red")+ggtitle("Heatmap of Sidewalk Cafe Licenses in Manhattan")+geom_text_repel(data=manhattan_names, aes(mean_lon, mean_lat, label=nbh),fontface="bold", size=3)

The heatmap view of the map is not a great one - it is difficult to tell the differences on the same scales in a heatmap. Looking at the same view, but by using geom_point again, we get the following view:

ggmap(manhattan_map)+geom_point(aes(x=LONGITUDE,y=LATITUDE, color=nbh),data=sidewalks_manhattan_active)+facet_wrap(~STATUS_CLASSIFICATION2)+ggtitle("Active and New Licenses in Manhattan") + guides(color=FALSE)+geom_text_repel(data=manhattan_names, aes(mean_lon, mean_lat, label=nbh),fontface="bold", size=3)

Once again, the data is not telling us too much since there are very few new requests. Perhaps a better way to categorize this data is to look at the years that applications were submitted.

ggplot(sidewalks_manhattan_active, aes(x=SUBMIT_YEAR))+geom_bar()

Since there are only one submission in both 2000 and 2014, I will be removing these outliers as potential mistakes where the submit dates were not updated once the licenses were renewed. Now we can look at a mosaic plot of the different neighborhoods and the years that the licenses were requested.

sidewalks_manhattan_active<-sidewalks_manhattan_active %>% filter(SUBMIT_YEAR > 2014)
manhattan_counts <- sidewalks_manhattan_active %>% group_by(nbh, SUBMIT_YEAR) %>% summarise(count=n())
manhattan_counts$SUBMIT_YEAR<-as.factor(manhattan_counts$SUBMIT_YEAR)
manhattan_counts$SUBMIT_YEAR<-factor(manhattan_counts$SUBMIT_YEAR, c("2017", "2016","2015"))
ggplot(manhattan_counts, aes(x=reorder(nbh, count), y=count, fill=SUBMIT_YEAR))+geom_bar(stat="identity",position=position_dodge())+coord_flip()+xlab("Neighborhood")

It makes sense that 2016 would have more applications than 2015, since many of the 2015 applications have been renewed by now. However, to get a better understanding of the distribution of areas, we can create three separate heatmaps.

map2015 <- ggmap(manhattan_map)+stat_density2d(aes(x=LONGITUDE,y=LATITUDE, fill=..level..),data=sidewalks_manhattan_active%>%filter(SUBMIT_YEAR=="2015"), geom="polygon", alpha=0.3)+scale_fill_gradient(low="yellow",high="red")+ggtitle("Manhattan Licenses Applied for in 2015")+geom_text_repel(data=manhattan_names, aes(mean_lon, mean_lat, label=nbh),fontface="bold", size=3)
map2015

map2016<-ggmap(manhattan_map)+stat_density2d(aes(x=LONGITUDE,y=LATITUDE, fill=..level..),data=sidewalks_manhattan_active%>%filter(SUBMIT_YEAR=="2016"), geom="polygon", alpha=0.3)+scale_fill_gradient(low="yellow",high="red")+ggtitle("Manhattan Licenses Applied for in 2016")+geom_text_repel(data=manhattan_names, aes(mean_lon, mean_lat, label=nbh),fontface="bold", size=3)
map2016

map2017 <- ggmap(manhattan_map)+stat_density2d(aes(x=LONGITUDE,y=LATITUDE, fill=..level..),data=sidewalks_manhattan_active%>%filter(SUBMIT_YEAR=="2017"), geom="polygon", alpha=0.3)+scale_fill_gradient(low="yellow",high="red")+ggtitle("Manhattan Licenses Applied for in 2017")+geom_text_repel(data=manhattan_names, aes(mean_lon, mean_lat, label=nbh),fontface="bold", size=3)
map2017

Across all three years, you can see how the concentrated area of licenses remains in the Greenwich and Village NoHo area. However, it looks like the number of applicants in the Upper West Side in 2017 has dropped.

queens_map <- get_map("Astoria, NY",  source = "google", zoom = 12, maptype="roadmap", color="bw") 

Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=Astoria,+NY&zoom=12&size=640x640&scale=2&maptype=roadmap&language=en-EN&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Astoria,%20NY&sensor=false

Listening on http://127.0.0.1:3509

Liquor License Data - Rohan

6. Conclusion - Rohan